通过NGSI-LD丰富重塑智能城市
Víctor González1, Laura Martín1, Juan Ramón Santana1
1Network Planning and Mobile Communications Laboratory, Universidad de Cantabria, 39005 Santander, Spain.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究利用像NGSI-LD这样的语义信息模型,增强了智能城市的数据处理. 先进的技术提高了数据的理解和链接,有利于以公民为中心的倡议和公共服务.
科学领域:
- 计算机科学 计算机科学
- 城市信息学 城市信息学
- 数据科学数据科学数据科学
背景情况:
- 物联网 (IoT) 数据和开放数据门户的扩散为公共和私营部门提供了巨大的潜力.
- 语义信息模型,如NGSI-LD,通过丰富和链接来增强数据价值,利用固有的上下文信息.
- 先进的数据处理技术对于协调和分析这些复杂的数据集和数据流至关重要.
研究的目的:
- 定义和开发先进的数据处理技术,在智能城市生态系统中协调数据集和数据流.
- 探索数据丰富和链接技术的潜力,以改变智慧城市的数据利用.
- 专注于以公民为中心的倡议,并通过实际例子展示这些技术的有效性.
主要方法:
- 使用基于链接数据建模和语义学的结构化方法.
- 开发一个数据丰富工具链框架,以NGSI-LD为中心.
- 展示实体转换以说明数据处理能力.
主要成果:
- 证明了基于NGSI-LD的数据丰富和链接技术的有效性.
- 揭示了重塑智能城市应用数据利用的潜力,特别是针对以公民为中心的倡议.
- 提供了实体转型的具体例子,突出了改进的数据处理.
结论:
- 拟议的数据处理技术显著改善了智能城市环境中的数据理解.
- 这些发现支持通过增强语义数据利用来推进智能城市倡议.
- 这项工作为未来研究语义数据和智能城市生态系统之间的协同作用奠定了基础.
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